
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "19"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 95 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 91 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	67.721622	103.592625	inf	inf	18.920874	34.148391	39.976955	110.144699	nan	nan	nan	0.000000	0.000000
2459593	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	262.520800	271.373768	inf	inf	30.743804	79.406348	80.780923	481.435677	nan	nan	nan	0.000000	0.000000
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.982020	0.375634	0.332729	3.823894	0.797769	0.480859	2.509628	0.188725	0.0298	0.0285	0.0017	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.111303	1.097517	4.142468	29.350792	16.431436	89.498735	-0.440888	4.031482	0.6605	0.6690	0.3761	3.960409	4.069401
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.415984	0.153352	4.186592	17.843263	0.920496	143.180506	-0.567358	10.530364	0.6626	0.6621	0.3781	4.489519	4.544679
2459589	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	332.118013	337.041128	inf	inf	17.875941	57.728355	65.262962	425.184714	nan	nan	nan	0.000000	0.000000
2459588	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.933473	0.065194	5.131881	45.151265	0.381633	30.578728	1.151537	15.858691	0.7679	0.7528	0.2231	6.311846	6.399707
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	348.709641	356.044851	inf	inf	24.905358	112.245323	183.442610	487.771965	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.451272	-0.513724	-0.284920	2.224269	2.197941	41.926969	0.431714	5.211788	0.6566	0.6842	0.3700	3.435338	3.628642
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.412004	11.209580	-1.179974	6.060203	-0.732625	5.433214	15.676061	8.299118	0.0321	0.0328	0.0013	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.147633	21.513087	4.494463	10.881013	10.843293	44.004521	0.616087	1.205296	0.6163	0.6350	0.3752	14.276283	10.664212
2459583	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.981403	-0.503100	1.596135	11.944597	0.024176	3.016011	2.219108	5.619514	0.6602	0.6631	0.3903	3.686233	3.606015
2459581	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.407709	0.417995	2.276998	14.857874	0.179086	19.448531	-0.484026	6.802875	0.6540	0.6563	0.3976	3.412632	3.387348
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.797858	-0.417397	3.026308	13.527920	1.504196	1.957111	-0.030146	3.910472	0.6671	0.6733	0.3844	4.083413	3.945626
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.289187	-0.197813	3.636671	16.554015	0.966702	8.245528	-0.721368	10.918521	0.6653	0.6705	0.3843	3.803483	3.817729
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.309812	0.449246	0.144303	0.967132	1.413052	1.375580	0.145553	2.618318	0.0296	0.0286	0.0022	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.325793	-0.400435	2.237218	11.397636	1.669229	149.906625	-0.886116	6.482885	0.6684	0.6633	0.3890	3.425707	3.179261
2459576	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.478032	-0.825329	1.651816	0.302045	1.018440	2.188270	-0.886544	0.631461	0.6775	0.6990	0.3878	3.239918	3.145369
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.136007	-0.228522	1.607942	4.527122	0.403180	84.562667	1.364809	5.073887	0.7375	0.7267	0.3478	0.000000	0.000000
2459574	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.145149	-0.907613	1.432154	0.227833	1.005002	2.403524	-1.149205	-0.196369	0.6930	0.7037	0.3762	3.522284	3.348098
2459573	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459572	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459571	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.530427	-0.488486	0.290830	-0.807576	1.666435	3.530026	-0.374042	0.012970	0.0299	0.0293	0.0013	nan	nan
2459570	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.658996	-1.069366	1.786692	0.807970	1.596942	11.545551	1.030790	1.559348	0.7447	0.7536	0.2589	3.891350	3.808935
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.585783	-0.012950	2.151993	17.291172	-0.160078	4.599653	0.722468	1.837994	0.6784	0.6557	0.4114	0.000000	0.000000
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	1.633766	1.633766	1.509412	1.509412	0.566250	0.566250	0.074197	0.074197	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.315601	-1.467773	2.832133	-2.950098	-0.244328	21.221153	-1.590678	1.061668	0.6863	0.7075	0.3964	2.447025	2.478521
2459563	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.223559	0.433345	23.691984	2.521617	12.169485	53.669280	-1.298257	5.393947	0.6635	0.6813	0.4062	0.000000	0.000000
2459562	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.251841	-1.509285	0.959890	-0.363679	-0.447605	40.481880	-1.328146	2.567413	0.6763	0.6965	0.3941	3.011485	2.858922
2459561	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.824892	-0.953888	1.489140	-1.048866	-0.204782	24.961805	-1.769389	1.799729	0.6780	0.7000	0.3918	3.434996	3.108372
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.286838	0.185015	1.086068	1.030497	0.020698	2.186257	-1.615816	2.643912	0.6735	0.6927	0.4033	3.527249	3.311671
2459559	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.885629	0.056773	1.656809	1.254368	0.214181	13.000755	-1.325073	3.219961	0.6811	0.7045	0.3937	2.489037	2.546709
2459558	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0313	0.0332	0.0043	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.747283	5.350164	13.386120	10.469906	12.476239	9.713508	-1.662205	-2.063613	0.6903	0.6998	0.3853	2.688756	2.746247
2459554	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6754	0.6865	0.4033	nan	nan
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6828	0.6932	0.4002	nan	nan
2459552	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459551	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459550	dish_ok	-	96.71%	96.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0520	0.0527	0.0021	nan	nan
2459549	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.872733	0.725038	12.910787	5.683711	13.154919	3.366289	-1.980669	13.439047	0.6767	0.6975	0.4110	2.379847	2.485614
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.032858	2.315448	24.260748	14.823731	0.436323	2.961208	-1.007574	-0.105435	0.7451	0.7494	0.3600	2.802639	2.636138
2459541	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.989984	0.865887	2.885868	0.995568	1.055307	3.569758	-0.811150	-0.708748	0.7364	0.7332	0.3600	2.932158	2.879559
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.138654	6.761386	20.033612	16.425773	12.191855	32.639421	-1.891386	-2.198580	0.7164	0.7294	0.4167	3.263109	3.198656
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.398204	-0.700842	1.757558	-1.741593	-0.134546	-0.354255	-0.303870	0.748600	0.7752	0.7608	0.3745	2.744450	2.706118
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.563491	-0.849566	0.802494	-2.085491	-1.005603	-0.903528	-0.618520	0.866825	0.7690	0.7628	0.3629	4.010082	4.629661
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.344962	0.645935	11.894726	3.463798	9.358072	2.057735	0.361381	2.331344	0.7114	0.7232	0.4011	2.820673	2.867390
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.434880	0.865132	15.649765	4.425942	12.524920	3.291244	-0.381051	0.986772	0.7094	0.7195	0.4018	3.043984	3.157419
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.735898	-1.201534	0.766031	-2.146898	-0.659601	3.020757	-0.852045	0.664009	0.7313	0.7385	0.3952	2.738794	2.760618
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.810262	1.445502	6.750754	1.032925	17.012394	21.548150	86.065938	5.425638	0.0332	0.0295	0.0013	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.483193	3.107443	10.711739	4.640511	11.267677	3.403443	9.698927	5.266907	0.8783	0.9138	0.2661	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.656246	1.820146	14.238839	6.692194	3.717347	8.732435	-0.984270	1.648838	0.8815	0.8520	0.2099	7.369255	10.034609

Antenna Season Report Notebook¶

Antenna 19 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 19, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 19: 2459619

Antenna 19: 2459618

Antenna 19: 2459617

Antenna 19: 2459616

Antenna 19: 2459615

Antenna 19: 2459614

Antenna 19: 2459613

Antenna 19: 2459612

Antenna 19: 2459611

Antenna 19: 2459610

Antenna 19: 2459609

Antenna 19: 2459608

Antenna 19: 2459607

Antenna 19: 2459605

Antenna 19: 2459604

Antenna 19: 2459603

Antenna 19: 2459602

Antenna 19: 2459598

Antenna 19: 2459597

Antenna 19: 2459596

Antenna 19: 2459595

Antenna 19: 2459594

Antenna 19: 2459593

Antenna 19: 2459592

Antenna 19: 2459591

Antenna 19: 2459590

Antenna 19: 2459589

Antenna 19: 2459588

Antenna 19: 2459587

Antenna 19: 2459586

Antenna 19: 2459585

Antenna 19: 2459584

Antenna 19: 2459583

Antenna 19: 2459582

Antenna 19: 2459581

Antenna 19: 2459580

Antenna 19: 2459579

Antenna 19: 2459578

Antenna 19: 2459577

Antenna 19: 2459576

Antenna 19: 2459575

Antenna 19: 2459574

Antenna 19: 2459573

Antenna 19: 2459572

Antenna 19: 2459571

Antenna 19: 2459570

Antenna 19: 2459569

Antenna 19: 2459566

Antenna 19: 2459565

Antenna 19: 2459564

Antenna 19: 2459563

Antenna 19: 2459562

Antenna 19: 2459561

Antenna 19: 2459560

Antenna 19: 2459559

Antenna 19: 2459558

Antenna 19: 2459556

Antenna 19: 2459552

Antenna 19: 2459551

Antenna 19: 2459549

Antenna 19: 2459542

Antenna 19: 2459541

Antenna 19: 2459540

Antenna 19: 2459536

Antenna 19: 2459535

Antenna 19: 2459534

Antenna 19: 2459533

Antenna 19: 2459532

Antenna 19: 2459530

Antenna 19: 2459527

Antenna 19: 2459524

Antenna 19: 2459523

Antenna 19: 2459522

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶